Motion denoising with application to time-lapse photography
Motion denoising with application to time-lapse photography
复制标题
运动去噪在延时摄影中的应用
DOI：
--
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
W. Freeman
中科院分区：
文献类型：
--
作者：
Michael Rubinstein;Ce Liu;Peter Sand;F. Durand;W. Freeman
运动可以在短时间尺度和长时间尺度上发生。我们引入了运动去噪，将短期变化视为噪声，长期变化视为信号，并重新渲染视频以揭示潜在的长期事件。我们演示了延时视频的运动去噪。传统延时图像的特征之一是风格化的急动，其中场景中的短期变化在视频中表现为小而烦人的抖动，通常会混淆感兴趣的潜在时间事件。我们将运动去噪应用于重新合成延时视频，显示场景的长期演变，去除了抖动的短期变化。我们发现，现有的滤波方法往往无法实现这一任务，并提出了一种新的计算方法去噪运动没有明确的运动分析。我们展示了一组具有挑战性的延时序列的实验结果。
Motions can occur over both short and long time scales. We introduce motion denoising, which treats short-term changes as noise, long-term changes as signal, and re-renders a video to reveal the underlying long-term events. We demonstrate motion denoising for time-lapse videos. One of the characteristics of traditional time-lapse imagery is stylized jerkiness, where short-term changes in the scene appear as small and annoying jitters in the video, often obfuscating the underlying temporal events of interest. We apply motion denoising for resynthesizing time-lapse videos showing the long-term evolution of a scene with jerky short-term changes removed. We show that existing filtering approaches are often incapable of achieving this task, and present a novel computational approach to denoise motion without explicit motion analysis. We demonstrate promising experimental results on a set of challenging time-lapse sequences.